{"as_of":"2026-08-08T16:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7663ef972d1ea7717c2f2d5b0f6c1f9bdc7bf9beef770ffee9ad5c012beb6d71","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:11:52.902473Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.22372/citation-record","integrity":"/paper/2506.22372/integrity","json":"/paper/2506.22372/citation-record.json","paper":"/paper/2506.22372"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:48.238741Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.238741Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:8402c5067b72c20457c1a2cebbcd990f361f037e0d8660521e4f2a8eac8a94e1","observation_id":"93cb607e-7210-4fd0-813e-d433f6b5bd44","resolution":{"observed_at":"2026-08-06T22:11:48.238741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05975","last_updated":"2024-03-09T18:24:58Z","snapshot_observed_at":"2026-07-06T17:42:03.971883Z","submitted_at":"2024-03-09T18:24:58Z","title":"Measuring Bias in a Ranked List using Term-based Representations","version":1},"cited_work":{"arxiv_id":"2403.05975","doi":"10.48550/arxiv.2403.05975","metadata_source":"pith","pith_arxiv_id":"2403.05975","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Measuring Bias in a Ranked List using Term-based Representations","venue":"cs.CL","work_id":"cd203fc4-0c8d-467b-8b4f-dc94f4fc996a","year":2024},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.385419Z"},"links":{"cited_paper":"/paper/2403.05975","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:caea88d5ccb80f525adfc094b2ffbd7a68979b5254a1b13c3e6eba09677e1f6b","observation_id":"4c32d0ed-6a89-4f8f-b0ce-468ba35b0f9f","resolution":{"observed_at":"2026-08-06T22:11:53.262146Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:48.479711Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.479711Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:c271ed89a2284ab9b072167c57a40f261695758dc7ef6de91d208e1b00486b14","observation_id":"688a8933-8c53-4ee7-bd74-df28e56636bd","resolution":{"observed_at":"2026-08-06T22:11:48.479711Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-28238-6_24","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":null,"venue":"Lecture notes in computer science","work_id":"8fb3e4ab-a84b-4945-bdb2-cdc3cd58d740","year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.617404Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:8deb3d628b03bd554afa50a085c53bdbde1572d57fa6b3a5cce73fa05acff188","observation_id":"28664e28-0159-493d-b742-6831fe9784e6","resolution":{"observed_at":"2026-08-06T22:11:53.136995Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:57.831313Z","title":null,"venue":null,"work_id":"ed529923-ef31-4e6f-8bbf-791f5eb1e1e2","year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.699361Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:0b87ece71a858c9feaddcbbb6a542250533e4c87bc4491823ff9a83f1832f608","observation_id":"95b55fb2-4f7a-4910-9e20-298fe0f3862c","resolution":{"observed_at":"2026-08-06T22:11:57.887326Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:48.780053Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.780053Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:4936be11c9097bbeda0449897ac1a4fc2910b1749c4f720a82ed2797e153cd22","observation_id":"09b31fb8-74b3-400a-bb20-d9807c938387","resolution":{"observed_at":"2026-08-06T22:11:48.780053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:48.865176Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.865176Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:6c21b1f8ed3c8c58832b5c7ebe58c60e05542e2bcec3ddaa7ffb5a1ff1b47e85","observation_id":"854e0be4-895e-4b5a-a115-f8425f0f4818","resolution":{"observed_at":"2026-08-06T22:11:48.865176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:48.965456Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.965456Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:008b27bd6121af3f5a0ae4fa1cef03aee8db412b8f5cc019fdc81cb4afbff7cc","observation_id":"3811a86f-8ed9-479c-b80b-b4fd4f7a31bb","resolution":{"observed_at":"2026-08-06T22:11:48.965456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:49.054373Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.054373Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:14ff4ab6b4ff0b85784fe6f3b92942fe6574d5cc516cef7c01dd501b73408f3a","observation_id":"eee4f551-5653-494a-b276-a0793d02959a","resolution":{"observed_at":"2026-08-06T22:11:49.054373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:49.162858Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.162858Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:af7e5199397ce30ad879305ed6358edd7d8a7ee09bd0a2176bf1c021b9196138","observation_id":"f1d0b496-6f74-4ab3-a067-b5f57c44f101","resolution":{"observed_at":"2026-08-06T22:11:49.162858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:49.268857Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.268857Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:35736d27931a4b197c695e2533a794e74213dd30bd66243419b2210c2e470886","observation_id":"03333a40-8cac-4784-9cb5-d4db6ff1273c","resolution":{"observed_at":"2026-08-06T22:11:49.268857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:57.531738Z","title":null,"venue":null,"work_id":"6d8254ce-7a28-49c9-82b1-25cf3a7e6373","year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.373398Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:72b2d518f6004d99571361a7a2731b918aab540c84df2f08ea990bacd95eb40a","observation_id":"6782ebb0-d735-4855-a932-efc86e0ba9ec","resolution":{"observed_at":"2026-08-06T22:11:57.697615Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.05558","last_updated":"2023-02-11T00:57:42Z","snapshot_observed_at":"2026-07-06T14:50:40.930413Z","submitted_at":"2023-02-11T00:57:42Z","title":"Overview of the TREC 2022 Fair Ranking Track","version":1},"cited_work":{"arxiv_id":"2302.05558","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.05558","snapshot_observed_at":"2026-08-06T22:11:53.607467Z","title":"Overview of the TREC 2022 Fair Ranking Track","venue":"cs.IR","work_id":"41d919e9-11e0-4d0a-95ac-c184eca8676b","year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.477569Z"},"links":{"cited_paper":"/paper/2302.05558","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:5c9f7804f252a046825273e2ae235249fb826aef1c66f0e854fece59407362a1","observation_id":"f0a281fd-9a53-4474-847f-06db1f92537c","resolution":{"observed_at":"2026-08-06T22:11:53.655284Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:57.219166Z","title":null,"venue":null,"work_id":"366d4906-4780-440d-91fb-a0a7cb5eaed3","year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.562299Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:f3f764cb73a3be7c6947069f7e86f2464b041cf5463687d93c7079e270099607","observation_id":"3fbe362b-7d3e-442c-8c1b-149525f53186","resolution":{"observed_at":"2026-08-06T22:11:57.401440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:49.757580Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.757580Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:612d424f62900519ce3c096da65eb24d304486805b1f749d10571a58ccefea60","observation_id":"8f42c474-a22e-48d6-98ca-44e263e815d1","resolution":{"observed_at":"2026-08-06T22:11:49.757580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:56.559362Z","title":null,"venue":null,"work_id":"716f38e5-26b7-4c35-90b0-69862bef8a50","year":2024},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.884639Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:894842a3414b73fc8b36297f1efa5242979a599e45c9e7b5cb77ba0e2e312d75","observation_id":"e5454383-5039-4c4f-90f7-ac0398ec5b20","resolution":{"observed_at":"2026-08-06T22:11:56.737562Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:49.980328Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.980328Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:ef675d7217d1639539caa2ddc44b2a57553110697eea4352dfdcf22ffdeb216e","observation_id":"2b411d2c-01bd-41a7-9eba-8c9968eb1559","resolution":{"observed_at":"2026-08-06T22:11:49.980328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:56.200959Z","title":null,"venue":null,"work_id":"713290c6-8c16-4d78-9c6b-ccc5af1c6512","year":1998},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.053571Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:acf318d97815479cb6aad91502492311f065f9f5ee49269e41efdd3273026171","observation_id":"22ef8451-d339-4e76-aef1-7d7208e54141","resolution":{"observed_at":"2026-08-06T22:11:56.378834Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:50.175048Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.175048Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:b4d2c0ea0f56e55e66795d9628ec8537a98b3aeb8c6a3fcade28b94dfdb23138","observation_id":"98d6a0d5-b72a-4db0-8488-3896a736dd7a","resolution":{"observed_at":"2026-08-06T22:11:50.175048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:50.297573Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.297573Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:427aad782f71f97cf7ecf4deaec0d72b706230ac217714c6bd29f71e6a66f19e","observation_id":"ec387f40-4124-4170-8fec-9b57bf049c26","resolution":{"observed_at":"2026-08-06T22:11:50.297573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:50.361241Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.361241Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:98ea8519791f8e1c6e008b2af7724ac2580da7152d5ff27495bc233920296152","observation_id":"730e1b83-90e4-4f52-8b4f-dea51b42a3d5","resolution":{"observed_at":"2026-08-06T22:11:50.361241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:55.806669Z","title":null,"venue":null,"work_id":"5b0509b3-7318-4895-bcf2-d25f2d32b30c","year":2022},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.441067Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:970c141feb8920b7514238843771e91c65f6aa648ec82610392c241c7d1241dc","observation_id":"69caace2-fcc0-4c2d-9ad2-7c658513b54d","resolution":{"observed_at":"2026-08-06T22:11:55.968869Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:55.514592Z","title":null,"venue":null,"work_id":"2731fbdd-c58b-4115-a35c-6668193b7219","year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.516668Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:fb9c4ed643bb3a2fd34a389e4898248b76608d9a5928b0eed6328691b5fb90b2","observation_id":"ec03038c-283a-427a-840a-44d6f90fac08","resolution":{"observed_at":"2026-08-06T22:11:55.651392Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:55.194338Z","title":"Kumar, Saurav Sahay, Sahisnu Mazumder, Eda Okur, Ramesh Manuvinakurike, Nicole Beckage, Hsuan Su, Hung-yi Lee, and Lama Nachman","venue":null,"work_id":"64c2d401-1195-45d2-8db1-1089a82e0f9e","year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.589573Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:c7685fe9ada4923c12ce30089b6a8a1b7a4dd7895b94059e33036650e7ae8aca","observation_id":"154b9b20-1bd5-4b38-9bc9-d7c6255aae12","resolution":{"observed_at":"2026-08-06T22:11:55.348288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:50.733213Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.733213Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:0b64c41151bc63050a1322c9e08665f8fd319affc76c448ffe3f6ec58c17c886","observation_id":"69650965-6fba-44c6-8b7e-5e9a62c11491","resolution":{"observed_at":"2026-08-06T22:11:50.733213Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:54.894055Z","title":null,"venue":null,"work_id":"6c18d5fa-5510-42e2-bc9a-e77f85b80a2c","year":2024},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.824150Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:3eb5594ab728664585dc55cce822e241001314531d20b07b2c07e49bb6274a1f","observation_id":"4de43c72-d7c3-4f78-b74f-d21c9ed3b9ad","resolution":{"observed_at":"2026-08-06T22:11:55.035936Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03907","last_updated":"2024-08-07T17:11:34Z","snapshot_observed_at":"2026-07-06T18:58:01.522372Z","submitted_at":"2024-08-07T17:11:34Z","title":"Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03907","snapshot_observed_at":"2026-08-06T22:11:50.660398Z","title":"doi:10.48550/arXiv.2408.03907 arXiv:2408.03907","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.660398Z"},"links":{"cited_paper":"/paper/2408.03907","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:a4cbf62dc2a5b119bcb10a627304bd74a5080c0dbff946d3a0f921b53e04f145","observation_id":"55785f28-e42a-4eba-acb7-2612f45659d1","resolution":{"observed_at":"2026-08-06T22:11:50.660398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.043386Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.043386Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:9ef699ab116198fdbd7eeb22804725f3793dca185298975d110463b7b8c90505","observation_id":"9286e9e9-c614-49aa-af24-624c724eb6f0","resolution":{"observed_at":"2026-08-06T22:11:51.043386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.229656Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.229656Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:7184139f46654b2d0854ffd32472a6bcd0025f8281cf0b64f3b360422b65c19e","observation_id":"b872ca9c-cf59-4b0d-a305-b8420d362c79","resolution":{"observed_at":"2026-08-06T22:11:51.229656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:54.628521Z","title":null,"venue":null,"work_id":"ed994ee8-fe9c-465d-98b7-105ce4aa57a0","year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.882291Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:f5dbf6c749c7386ed73680bff17fa779e6385f8ff6ec7fc96ffc3e278e6e9918","observation_id":"2d444ed8-255d-411e-a086-4f7353dba407","resolution":{"observed_at":"2026-08-06T22:11:54.749272Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:50.961279Z","title":"In Find- ings of the Association for Computational Linguistics: EMNLP 2020 , Trevor Cohn, Yulan He, and Yang Liu (Eds.)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:50.961279Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:fb6400ea08157895220015e2b2119123e06be290061f492868f56cf148d45cac","observation_id":"d5cd3ac5-bbf8-4db3-b0fe-e3c1f3e383de","resolution":{"observed_at":"2026-08-06T22:11:50.961279Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.486444Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.486444Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:cc71d8d43ac968ce11589f06151baaaddbab43cba3eee19534bc7e1b29457778","observation_id":"25c309b6-fa72-4a5a-a5d0-40bc6c81ed56","resolution":{"observed_at":"2026-08-06T22:11:51.486444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.553139Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.553139Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:aea75580d94991a6c569c229f522446a5735634b58b9ca2c6de98eadcef98b44","observation_id":"94a8ffca-6ee6-4b66-8024-b4630f4b1703","resolution":{"observed_at":"2026-08-06T22:11:51.553139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:54.092413Z","title":null,"venue":null,"work_id":"ed648ec5-e7e1-49ec-81bb-fed470ecc32b","year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.649534Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:bf537db337be33de803356a36a8c507fb69ac320ce3601d1b2292c77c6d1e6e0","observation_id":"c624ea0d-e079-4d04-8a8c-e6c8e84de3d6","resolution":{"observed_at":"2026-08-06T22:11:54.168300Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.325063Z","title":"Bowman, and Rachel Rudinger","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.325063Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:e28320973871781fecfeab4cee21d890f183e3a11acf84b55a4cc3903e3dbc8e","observation_id":"42f9653e-b38c-4960-a0ba-1127e017153e","resolution":{"observed_at":"2026-08-06T22:11:51.325063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.414608Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.414608Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:4225dcc37fd7613fa0501451943acbd38d9fd04bbd6781397d6edbe25a60d4f7","observation_id":"090bee3b-f560-4162-93d2-3c3809057941","resolution":{"observed_at":"2026-08-06T22:11:51.414608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.979444Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.979444Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:0464d0b9d24a00bf41d69e2ed5e7b5a4d6be4af41f171324c02b46736697e7aa","observation_id":"8c4a61ba-bfbf-4804-a9b0-6db80b34dad2","resolution":{"observed_at":"2026-08-06T22:11:51.979444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:53.935344Z","title":null,"venue":null,"work_id":"5b017171-d8f5-4dd8-838e-a7dc70c018f6","year":2024},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.057302Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:6c24c4c437b8668a0df913097f8ce2cba0947bb7cdc138654d9677446e69a7a1","observation_id":"aedd4081-10da-4358-a862-8efb26eb06c0","resolution":{"observed_at":"2026-08-06T22:11:53.952919Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.148319Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.148319Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:5cc7444ad89144256fdb69a68b86c8665bbcf53384e978ba4fc87d88046b366e","observation_id":"8547b306-72ec-43b0-99fc-3ffaafb7a6e0","resolution":{"observed_at":"2026-08-06T22:11:52.148319Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.217792Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.217792Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:7ac56a51fe0ce897f57c5a23e4372f9af897f3251efe2b06ca8fd302d44f51be","observation_id":"1c209621-ca1b-4d4b-87b0-85b1fa1b6313","resolution":{"observed_at":"2026-08-06T22:11:52.217792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.817790Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.817790Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:a62e0ce84e662741099c4d8270325264b5306cd4f10fe40c5859f663599fdb02","observation_id":"f2db1173-dab1-4db3-af93-6486f4f7dde0","resolution":{"observed_at":"2026-08-06T22:11:51.817790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.904421Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.904421Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:cd282fbe1147aa17e917cc0b39cfd55b049e3ba5d3520d289e220291ba31e5f6","observation_id":"8cc3ab83-170f-4f80-a252-a28797b3dbb9","resolution":{"observed_at":"2026-08-06T22:11:51.904421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.10437","last_updated":"2019-05-13T05:42:50Z","snapshot_observed_at":"2026-07-06T07:29:49.559045Z","submitted_at":"2019-01-29T18:25:54Z","title":"Quantifying the Impact of User Attention on Fair Group Representation in Ranked Lists","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.10437","snapshot_observed_at":"2026-08-06T22:11:52.469226Z","title":"Robertson, Alan Mislove, and Christo Wilson","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.469226Z"},"links":{"cited_paper":"/paper/1901.10437","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:1d81d4dbae9f73407398d91584fc3d67fe3f3b4dce0b6672f2534cc94b65e910","observation_id":"b83828b9-f48c-47ff-98f3-1cc55921ee73","resolution":{"observed_at":"2026-08-06T22:11:52.469226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.554782Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.554782Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:a1544fc1a6eb3e3ce24f864368f34cad7c42ecb59a5726ed15d5eb5ff93abf38","observation_id":"0b5a32c6-f3c3-4d79-b7d3-5a53ef1e644d","resolution":{"observed_at":"2026-08-06T22:11:52.554782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.643638Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.643638Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:a981d32b0432c1de51f97f8ea84536ebe3f27781e3f66bd8433ad997339e82c0","observation_id":"c630c421-5966-43ab-b057-9d9a22fc1abe","resolution":{"observed_at":"2026-08-06T22:11:52.643638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:53.844618Z","title":null,"venue":null,"work_id":"42d8a3b0-9dcc-46cd-b0bd-bd6fdbe4c3ad","year":2025},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.705706Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:bbbb41c7eec11d56194b09d99c03dfca5df12d2bb7c9f7b1343773d64ea83b5b","observation_id":"2e2ebbd5-eac2-4361-a9c4-ae5796c615df","resolution":{"observed_at":"2026-08-06T22:11:53.875148Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.293682Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.293682Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:805fa172c1eebeb1661f1c9565ac317460e814b5cc7301d8271108fb5b26bd20","observation_id":"8910426d-b948-4bdb-892d-90ce6176d8b6","resolution":{"observed_at":"2026-08-06T22:11:52.293682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.378433Z","title":"Nguyen, and Katrin Kirchhoff","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.378433Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:6a9547006c0e2e583ddf7b555eb152e3737fd131b1d6140506204ae36c439bfd","observation_id":"c9413813-a670-4b70-bf78-52c4ebb817a2","resolution":{"observed_at":"2026-08-06T22:11:52.378433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.902473Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.902473Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:b23c2922e6d6bb9fe64841e795a61c956a818f24cdd1ba2aefd16c8a75b26ca6","observation_id":"1e4dd199-c7b4-4a35-9f17-edafca742de8","resolution":{"observed_at":"2026-08-06T22:11:52.902473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:52.783481Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.783481Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:682c5a150aa39c264a5ae03dff4d630c9ba865ac32376a29e620a34d42e4899f","observation_id":"cdc46161-f1f1-497f-b994-6a608db26352","resolution":{"observed_at":"2026-08-06T22:11:52.783481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.06032","last_updated":"2021-03-02T21:04:26Z","snapshot_observed_at":"2026-07-06T10:03:46.733926Z","submitted_at":"2020-10-12T21:15:29Z","title":"Measuring and Reducing Gendered Correlations in Pre-trained Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.06032","snapshot_observed_at":"2026-08-06T22:11:52.837799Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:52.837799Z"},"links":{"cited_paper":"/paper/2010.06032","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:7608266d498e45b6f7230952b6809ae1cba9697b9a8c50d3c39f7941a2004eab","observation_id":"8fa49dc5-bcb0-457b-add9-ee2069436618","resolution":{"observed_at":"2026-08-06T22:11:52.837799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:51.738060Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":1967,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.738060Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:e70cbccc3aeb8d35b4a816aacc2f3cc8befbaa66df2dc930f7fd88563cdb0ce8","observation_id":"00b82660-abd4-4778-ace5-71171025859e","resolution":{"observed_at":"2026-08-06T22:11:51.738060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:56.885883Z","title":"Information Processing & Management 57, 6 (2020), 102377","venue":null,"work_id":"2a458804-28a1-4593-b1dc-306a6c2013f0","year":2020},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:49.674480Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:e5145410c6951ca8d3dc171e8335f9c1fec84cfdefeccf86145e086b372896c7","observation_id":"06b8e2fb-fcff-4d32-b0e9-7060dce306b1","resolution":{"observed_at":"2026-08-06T22:11:57.068837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:11:54.310715Z","title":"In International Conference on Machine Learning","venue":null,"work_id":"3a16e074-1d1b-4dbb-86e9-f283ced9722e","year":null},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:51.119507Z"},"links":{"citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:c17c6bbe5ecefad1415694e2b3f0662be523e6f3761c66d82649e3258e5f1c4d","observation_id":"30bc8779-96b7-456a-ae13-09dd5332ab56","resolution":{"observed_at":"2026-08-06T22:11:54.455043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05600","last_updated":"2024-05-09T07:39:19Z","snapshot_observed_at":"2026-07-06T18:11:57.100591Z","submitted_at":"2024-05-09T07:39:19Z","title":"Can We Use Large Language Models to Fill Relevance Judgment Holes?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05600","snapshot_observed_at":"2026-08-06T22:11:48.297439Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T22:11:48.297439Z"},"links":{"cited_paper":"/paper/2405.05600","citing_paper":"/paper/2506.22372"},"observation_digest":"sha256:677e575e748d807d02e63f7c27bf39b4871bae54feef2eaa8b5714171027dada","observation_id":"f6d2a5e3-6d9b-48e0-b5e0-1141a96a4997","resolution":{"observed_at":"2026-08-06T22:11:48.297439Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.22372","last_updated":"2025-06-27T16:39:12Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-06T22:03:08.535042Z","submitted_at":"2025-06-27T16:39:12Z","title":"Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":3,"verified_fuzzy":3},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2506.22372."}